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Available techniques employ various models and different types of input data to produce accurate predictions. However, from a practical perspective, explainability is another important requirement besides accuracy since predictive process monitoring techniques frequently support decision-making in critical domains. Techniques from the area of explainable artificial intelligence (XAI) aim to provide this capability and create transparency and interpretability for black-box ML models. While several explainable predictive process monitoring techniques exist, none of them leverages textual data. This is surprising since textual data can provide a rich context to a process that numerical features cannot capture. Recognizing this, we use this paper to investigate how the combination of textual and non-textual data can be used for explainable predictive process monitoring and analyze how the incorporation of textual data affects both the predictions and the explainability. Our experiments show that using textual data requires more computation time but can lead to a notable improvement in prediction quality with comparable results for explainability.<\/jats:p>","DOI":"10.1007\/978-3-031-27815-0_14","type":"book-chapter","created":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T10:03:04Z","timestamp":1679738584000},"page":"190-202","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["On the\u00a0Potential of\u00a0Textual Data for\u00a0Explainable Predictive Process Monitoring"],"prefix":"10.1007","author":[{"given":"Christian","family":"Warmuth","sequence":"first","affiliation":[]},{"given":"Henrik","family":"Leopold","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,26]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","volume":"58","author":"A Barredo Arrieta","year":"2020","unstructured":"Barredo Arrieta, A., et al.: Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. 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